Tel.: +43 (0) 662 8044 3770
Bayesian methods, big data, multivariate time series analysis, statistical and machine learning, economic policy transmission channels, forecasting
Niko Hauzenberger is a post-doctoral researcher at the Department of Economics, University of Salzburg. He received his Ph.D. in Economics from WU Vienna in September 2020. His research interests center on developing new econometric methods by drawing from literature on statistical learning and Bayesian econometrics, and on applying these state-of-the-art techniques to high-dimensional macroeconomic and financial data. He has published in the in the Journal of Applied Econometrics, the Journal of Business & Economic Statistics, the Journal of Economic Behavior & Organization and the Scandinavian Journal of Economics.
Hauzenberger, Niko, Florian Huber, Gary Koop, and Luca Onorante. “Fast and flexible Bayesian inference in time-varying parameter regression models.” Journal of Business & Economic Statistics, 2021, forthcoming. doi
Hauzenberger, Niko, Florian Huber, and Luca Onorante. “Combining shrinkage and sparsity in conjugate vector autoregressive models.” Journal of Applied Econometrics, 2021, 36.3, 304-327. doi
Hauzenberger, Niko, Michael Pfarrhofer, and Anna Stelzer. “On the effectiveness of the European Central Bank’s conventional and unconventional policies under uncertainty.” Journal of Economic Behavior & Organisation, 2021, 191, 822-845. doi
Hauzenberger, Niko and Michael Pfarrhofer. “Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy.” Scandinavian Journal of Economics, 2021, 123.4, 1261-1291. doi
Hauzenberger, Niko. “Flexible mixture priors for large time-varying parameter models.” Econometrics and Statistics, 2021, 20, 87-108. doi